Image-to-Image Translation for Domain Adaptation

JoliGAN provides models and machine learning tools for unpaired image to image translation and domain adaptation.

• Multiple models based on adversarial generation: CycleGAN, CyCADA, CUT and more
• Many generator architectures such as styleGAN2 decoder / mobile resnet, attention resnet,...
• Semantic consistency allowing to perform domain adaptation e.g. from synthetic to real-like data

Use cases

• Image to image translation while preserving semantic

• Image to image translation to cope with scarce data

Models

Generator architectures

Quick Start

Prerequisites

• Linux
• Python 3
• CPU or NVIDIA GPU + CUDA CuDNN

Installation

Clone this repo:

git clone --recursive https://github.com/jolibrain/joliGAN.git
cd joliGAN

Install PyTorch and other dependencies (torchvision, visdom and dominate, FID).
For pip users, please type the command pip install -r requirements.txt.

JoliGAN training

With a dataset located in directory dataroot:

• Train with cut :

You can modify the hyperparameters in ./scripts/train_cut.sh and then use the following line command.

bash ./scripts/train_cut.sh dataroot

• Train with cut and labels :

You can modify the hyperparameters in ./scripts/train_cut_semantic.sh and then use the following line command.

bash ./scripts/train_cut_semantic.sh dataroot

• Train with cut and mask labels :

You can modify the hyperparameters in ./scripts/train_cut_semantic_mask.sh and then use the following line command.

bash ./scripts/train_cut_semantic_mask.sh dataroot

• Train with cycleGAN :

You can modify the hyperparameters in ./scripts/train_cyclegan.sh and then use the following line command.

bash ./scripts/train_cyclegan.sh dataroot

• Train with cycleGAN and labels :

You can modify the hyperparameters in ./scripts/train_cyclegan_semantic.sh and then use the following line command.

bash ./scripts/train_cyclegan_semantic.sh dataroot

• Train with cycleGAN and mask labels :

You can modify the hyperparameters in ./scripts/train_cyclegan_semantic_mask.sh and then use the following line command.

bash ./scripts/train_cyclegan_semantic_mask.sh dataroot

JoliGAN server

Ensure everything is installed

python3 -m pip install fastapi uvicorn

Then run server:

server/run.sh --host localhost --port 8000

Tests

To launch tests before new commits:

bash scripts/run_tests.sh /path/to/dir

Docker build

To build a docker for joliGAN server:

docker build -t jolibrain/joligan_build -f docker/Dockerfile.build .
docker build -t jolibrain/joligan_server -f docker/Dockerfile.server .

To run the joliGAN docker:

nvidia-docker run jolibrain/myjoligan

Code format

If you want to contribute please use black code format. Install:

pip install black 

Usage :

black .

If you want to format the code automatically before every commit :

pip install pre-commit
pre-commit install

Authors

JoliGAN is created and maintained by Jolibrain.

Our code is inspired by pytorch-CycleGAN-and-pix2pix, CUT, AttentionGAN, MoNCE among others.